Welcome

This is part of a series of introductory tutorials to work with geo-spatial data in R. I prepared these tutorials as a intuitive “hands on” introduction, but I provide links for those interested in more background and theory.

In this tutorial, you will follow some steps to read spatial data in vector format with package sf (simple features), estimate the spatial correlation of variables associated with these points and use a semivariogram model to apply spatial interpolation following the indicator kriging approach.

library(sf)
library(gstat)
library(ggplot2)
library(tmap)

Protected areas in Bhutan

We will focus on Bhutan, a landlocked South Asian country situated in the Eastern Himalayas, between China in the north and India in the south.

A mountainous country, Bhutan is known locally as “Druk Yul” or “Land of the Thunder Dragon”.

We will take a look at the protected areas of Bhutan: https://www.protectedplanet.net/country/BTN

We use the function wdpa_fetch

wdpa_query <- wdpa_fetch("BTN") 
wdpa_query

Buthan_PAs <- wdpa_query %>% 
          select(ORIG_NAME,DESIG,IUCN_CAT,REP_AREA,STATUS_YR,MANG_AUTH)

mapview(Buthan_PAs,
        zcol = "STATUS_YR")

tmap_mode("view")

## tmap mode set to interactive viewing

tm_shape(Buthan_PAs) +
  tm_polygons("STATUS_YR")

Royal_Manas <- Buthan_PAs  %>% filter(ORIG_NAME %in% "Royal Manas National Park") 
JSW_NP <- Buthan_PAs  %>% filter(ORIG_NAME %in% "Jigme Singye Wangchuck National Park")

Elevation data

elev_data <- get_elev_raster(JSW_NP, z = 9)
JSW_NP_DEM <- rast(elev_data)

plot(JSW_NP_DEM)
contour(JSW_NP_DEM,add=T)
lines(JSW_NP,  lwd=3, alpha=.6)